Genetic Interaction Screening via Iterative Gene Selection
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Solution Overview
Problem
Current genetic interaction screening methods are resource-intensive and inefficient, especially in larger genomes or multicellular organisms, as they require exhaustive screening across all possible genetic interactions, which is often infeasible, and existing optimization strategies do not effectively address the unique behavior of genetic interactions.
Innovation Solution
The development of the COMPRESS-GI method, which uses computer algorithms to select an informative set of genes for screening, optimizing the precision-recall statistics of genetic interaction profile similarity networks, thereby reducing the number of genes needed to be screened while maintaining or improving information content, and the COMPRESS-GI-LAF approach for iterative genetic interaction screening, which prioritizes genes based on their informativeness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If exhaustive screening across all possible genetic interactions is conducted, then complete genetic interaction network data is obtained, but resource consumption and time required increase significantly
Solution Approach 1:
The patent segments the complete genetic interaction screening task into multiple iterative rounds. In each round, a subset of genes is screened against the entire array, and results are used to update the genetic interaction network incrementally. This divides the exhaustive screening process into manageable segments that can be performed sequentially, reducing resource consumption while maintaining information quality.
Solution Approach 2:
The patent performs preliminary actions by using computational algorithms to predict and prioritize gene pairs likely to exhibit genetic interactions before conducting actual screenings. This preliminary computational analysis guides the selection of gene pairs for experimental screening, allowing the system to focus resources on high-probability interactions and avoid exhaustive screening of all possible pairs.
2Productivity
If the number of genes screened is reduced to improve efficiency, then screening time and resources decrease, but information content and precision may be lost
Solution Approach 1:
The patent implements feedback mechanisms where the results from each screening round are fed back into the system to update the genetic interaction network and guide subsequent screening decisions. The precision-recall statistics are continuously monitored and used to adjust the selection of genes for the next screening round, ensuring that information quality is maintained even as the number of screened genes is reduced.
Solution Approach 2:
The patent dynamically changes screening parameters such as the number of query genes, the selection criteria for array genes, and the confidence thresholds for identifying genetic interactions. These parameter adjustments are made based on feedback from previous rounds and the desired precision-recall performance, allowing the system to optimize the balance between screening efficiency and information quality.
3Productivity
If iterative screening with gene prioritization is implemented, then screening efficiency improves, but algorithm complexity increases
Solution Approach 1:
The patent implements self-service mechanisms where the screening system automatically selects and prioritizes genes for subsequent rounds based on the results from previous rounds. The algorithm autonomously updates the genetic interaction network, identifies high-priority gene pairs, and generates the next screening plan without requiring manual intervention, thereby managing algorithmic complexity through automation.
Solution Approach 2:
The patent employs dynamic algorithms that adapt the screening strategy based on real-time results and the evolving genetic interaction network. The gene prioritization criteria and screening parameters are dynamically adjusted during the iterative process, allowing the system to respond to emerging patterns and optimize efficiency while managing complexity through adaptive behavior rather than fixed complex rules.
Data Source
AI summary
Disclosed in some examples are methods including selecting a first plurality of single gene mutants from a pool of possible single gene mutants of an organism. The first plurality of single gene mutants is less than a number of possible single mutants. A computer processor is used to iteratively select a second plurality of single gene mutants by selecting single gene mutants from the pool of possible single gene mutants that increases a sum of products of similarities between the first plurality of single gene mutants and corresponding functional relationships. The second plurality of single gene mutants is larger in number than the first plurality of single gene mutants, and wherein the second plurality of single gene mutants is less than the number of possible single gene mutants of the organism. A set of genes is outputted comprising the first and second pluralities of single gene mutants.